Overview

This is a combination of these analyses used for loop. Code is a combination of Daniela and Shuyings code. Might add Nicks later

Below is a list of sections included here. Including summaries of the white matter analyses and completed figures.



Methods

Behavior

  • group differences?

Hormones

Shapiro-Wilk normality tests were conducted to assess violations of normality of the independent and dependent variables before conducting correlations between spatial navigation dependent variables and sex hormones. To control for chronological age while assessing the relationship between sex steroid hormones and navigational strategy, partial Spearman rank correlations were conducted if the Shapiro-Wilk normality tests were statistically significant (p < 0.05); otherwise, Pearson correlations were conducted. Based on existing evidence of sex hormones’ influence on navigation from the animal literature and strong a priori predictions that estradiol would be positively associated with navigation performance, and follicle-stimulating hormone would show an opposing effect, we conducted one-tailed analyses for these tests, controlling for chronological age. Two-tailed analyses were used for hormones without a strong a priori hypothesis (progesterone, testosterone). Men and women were analyzed separately. For men, we conducted a one-tailed correlation for testosterone to be in the positive direction, while we conducted a two-tailed correlation for testosterone for women.

Hippocampal Subfields

** notes as of 2025 ** - T1 HP volume extracted using freesurfer recon all. - Corrected using TIV from free surfer

Total hippocampus (from T1-weighted whole brain scans) and hippocampal subfield volumes were corrected using participant’s total intracranial volume (TIV) to remove size Figure 4.1. Investigating volumetric differences using segmentation of the medial temporal lobe and total hippocampus region. (A) Sample slice of the medial temporal lobe cortex and hippocampus segmented into hippocampal subfields using the Automatic Segmentation of Hippocampal Subfields software. Labeled subfields include: CA1 (cornu ammonis), CA2/3, DG (dentate gyrus), SUB (subiculum), ERC (entorhinal cortex), PRC (perirhinal cortex), and PHC (parahippocampal cortex). Total hippocampus is computed by aggregating subfields CA1, CA2/3, DG, and SUB. Medial temporal lobe is computed by aggregating all the subfields. (B) Women (n = 74, M = 0.56) tend to have larger T1 total hippocampal volume than men (n = 32, M = 0.48; t(68) = 9.72, p < 0.001). Boxplot endpoints indicate the 25th and 75th percentile, and the black line within the boxplot indicates the median value while the black point within the boxplot indicates the mean value. p-values: *** p < 0.001.

bias in comparisons. In addition to the total hippocampal volume from the T1-weighted scans, another measure of total hippocampal volume from the T2-weighted hippocampal subfield scans was calculated by taking the sum of the CA1, CA2/3, dentate gyrus, and subiculum subfield volumes after adjusted for TIV. These structures make up the hippocampus region based on the anatomical components of the medial temporal lobe system (Squire et al., 2004; Squire & Zola-Morgan, 1991). An average of the left and right grey matter volume (mm3) for the total hippocampus and the individual subfields was used for analysis. For all the statistical tests mentioned, corrections for multiple comparisons were performed using Benjamini, Hochberg, and Yekutieli p-adjustments to control the false discovery rate.

Cortical Thickness

  • Conduct Spearman correlations
  • Benjamini Hotchkins control
  • Going to try Q value control

Diffusion


Results

Reading in and prepping our data

Reading in our main LOOP CSV and creating large dataframes for midlife and young




Hippocampal Analyses

Midlife

List of columns

The list below are from shuyings original raw data. We will ignore the old T1. The T2 here are already corrected for TIV.

columns
t1_vbm_tiv
t1_vbm_gmv
t1_vbm_wmv
t1_vbm_csf
t1_vol_left_hipp_aal_2d_d1_r
t1_vol_right_hipp_aal_2d_d1_r
t1_vol_left_hipp_aal_3d_d1_s
t1_vol_right_hipp_aal_3d_d1_s
t2hipp_vol_avg_ca1
t2hipp_vol_avg_ca23
t2hipp_vol_avg_dg
t2hipp_vol_avg_erc
t2hipp_vol_avg_phc
t2hipp_vol_avg_prc
t2hipp_vol_avg_sub
t2hipp_vol_left_ca1
t2hipp_vol_left_ca23
t2hipp_vol_left_dg
t2hipp_vol_left_erc
t2hipp_vol_left_phc
t2hipp_vol_left_prc
t2hipp_vol_left_sub
t2hipp_vol_right_ca1
t2hipp_vol_right_ca23
t2hipp_vol_right_dg
t2hipp_vol_right_prc
t2hipp_vol_right_sub

Creating HP specific Dataframe : midlife_HP_df

Total N - 43

# Let's create a clean df to work with here and include only the columns we want 

midlife_HP_df <-
  midlife_raw_df %>%  dplyr::select(
    "subject_id",
    "sex",
    "age_spatial_years",
    "repo_status",
    "loop_pe_rad3_m",
    "loop_pe_avg_m",
    "loop_de_rad3_degree",
    "loop_de_avg_degree",
    "loop_ae_rad3_degree",
    "loop_ae_avg_degree",
    "t1_vbm_tiv",
    "t1_vbm_gmv",
    "t1_vbm_wmv",
    "t1_vbm_csf",
    "t1_vol_left_hipp_aal_2d_d1_r",
    "t1_vol_right_hipp_aal_2d_d1_r",
    "t1_vol_left_hipp_aal_3d_d1_s",
    "t1_vol_right_hipp_aal_3d_d1_s",
    "t2hipp_vol_avg_ca1",
    "t2hipp_vol_avg_ca23",
    "t2hipp_vol_avg_dg",
    "t2hipp_vol_avg_erc",
    "t2hipp_vol_avg_phc",
    "t2hipp_vol_avg_prc",
    "t2hipp_vol_avg_sub",
    "t2hipp_vol_left_ca1" ,
    "t2hipp_vol_left_ca23",
    "t2hipp_vol_left_dg",
    "t2hipp_vol_left_erc",
    "t2hipp_vol_left_phc",
    "t2hipp_vol_left_prc",
    "t2hipp_vol_left_sub"  ,
    "t2hipp_vol_right_ca1",
    "t2hipp_vol_right_ca23",
    "t2hipp_vol_right_dg",
    "t2hipp_vol_right_prc",
    "t2hipp_vol_right_sub",
    "Left-Hippocampus",
    "Right-Hippocampus",
    "eTIV"
  ) %>% mutate(avg_t1_hipp = (.$`Left-Hippocampus` + .$`Right-Hippocampus`) /
                 2) %>% filter(!is.na(eTIV)) # Need to make sure we remove subj without scan # N=43
  
midlife_HP_female_df <- midlife_HP_df %>% filter(sex=="Female")

midlife_HP_male_df <- midlife_HP_df %>%  filter(sex== "Male")

Checking normality

  • Degrees traveled for all midlife is not normally distributed (p=0.04) and degrees traveled for men only ( p = 0.0001979)
  • Position error was normally distributed for all groups
  • Angular error rad3 was normally distributed for alll groups
  • Angular error average for midlife was not normally distributed (p = 0.0007472) and it was not normally distributed for women only ( p-value = 0.004246)
knitr::kable(normality_midlife_HP) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "300px")
statistic pvalue method variable
0.979930524959475 0.701056792400381 Shapiro-Wilk normality test midlife_HP_df\(loop_pe_rad3_m </td> </tr> <tr> <td style="text-align:left;"> 0.969511101559754 </td> <td style="text-align:left;"> 0.632694357678411 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_female_df\)loop_pe_rad3_m
0.980825888472044 0.979309814230114 Shapiro-Wilk normality test midlife_HP_male_df\(loop_pe_rad3_m </td> </tr> <tr> <td style="text-align:left;"> 0.958951620422575 </td> <td style="text-align:left;"> 0.126913086034452 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_df\)loop_pe_avg_m
0.932701897868634 0.0898823823710984 Shapiro-Wilk normality test midlife_HP_female_df\(loop_pe_avg_m </td> </tr> <tr> <td style="text-align:left;"> 0.959742019099185 </td> <td style="text-align:left;"> 0.626635989107761 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_male_df\)loop_pe_avg_m
0.939301920132143 0.0360882364913906 Shapiro-Wilk normality test midlife_HP_df\(loop_de_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.984861143748585 </td> <td style="text-align:left;"> 0.961650420612648 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_female_df\)loop_de_rad3_degree
0.673250270692041 0.000197903436428711 Shapiro-Wilk normality test midlife_HP_male_df\(loop_de_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.978359619965325 </td> <td style="text-align:left;"> 0.583947583573168 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_df\)loop_de_avg_degree
0.980483557611062 0.88401546577215 Shapiro-Wilk normality test midlife_HP_female_df\(loop_de_avg_degree </td> </tr> <tr> <td style="text-align:left;"> 0.926780583374995 </td> <td style="text-align:left;"> 0.192192441466166 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_male_df\)loop_de_avg_degree
0.969348782601871 0.358539147520987 Shapiro-Wilk normality test midlife_HP_df\(loop_ae_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.961043339052538 </td> <td style="text-align:left;"> 0.435645190326644 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_female_df\)loop_ae_rad3_degree
0.966162297290397 0.821589067567862 Shapiro-Wilk normality test midlife_HP_male_df\(loop_ae_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.892510350444314 </td> <td style="text-align:left;"> 0.000747247688039471 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_df\)loop_ae_avg_degree
0.873682962552045 0.00424606303747878 Shapiro-Wilk normality test midlife_HP_female_df\(loop_ae_avg_degree </td> </tr> <tr> <td style="text-align:left;"> 0.922588887317344 </td> <td style="text-align:left;"> 0.163331176169011 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> midlife_HP_male_df\)loop_ae_avg_degree



Behavior summarized

loop_summarystats <- midlife_HP_df  %>% 
  group_by(sex) %>% 
  summarize(n_subject = n(),
            age_mean = mean(age_spatial_years),
            age_Sd = sd(age_spatial_years),
            AE_rad3 = mean(loop_ae_rad3_degree,na.rm=TRUE),
            AE_avg = mean(loop_ae_avg_degree ,na.rm=TRUE),
            PE_rad3 = mean(loop_pe_rad3_m,na.rm=TRUE),
            PE_avg = mean(loop_pe_avg_m,na.rm=TRUE),
            DT_rad3 = mean(loop_de_rad3_degree,na.rm=TRUE),
            DT_avg = mean(loop_de_avg_degree,na.rm=TRUE)) %>% as.data.frame() 
knitr::kable(loop_summarystats) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "200")
sex n_subject age_mean age_Sd AE_rad3 AE_avg PE_rad3 PE_avg DT_rad3 DT_avg
Female 26 50.23077 3.701974 69.8513 58.99396 3.130604 1.846265 393.4124 375.6190
Male 17 50.35294 3.920159 64.9243 51.29080 2.871218 1.632895 328.1449 338.1785

T1 adjust correlations

Shuying originally did an adjustment. so that’s waht we’re doing below. First we need to correct the T1 hippocampal volumes for TIV. T1 volumes are coming form freesurfer but TIV is coming from VBM

The reason i am using VBM from shuying and not freesurfer is because the freesurfer metrics are different and i’m not sure that the function/math shuying used previosly works for the free surfer metric.

#v contains adjusted hip
# 1 Create function for apply to variables
dividebyTIV <- function(x, na.rm = FALSE) (x/midlife_HP_df$t1_vbm_tiv)

# 2 Let's correct by mutating the columns using the TIV from freesurfer

midlife_HP_df_adj <- midlife_HP_df %>% mutate_at(vars(avg_t1_hipp, `Left-Hippocampus`, `Right-Hippocampus`),
            dividebyTIV) %>% 
  
  # multiplying to get proportions 
   mutate(avg_t1_hipp = avg_t1_hipp*100,
         `Left-Hippocampus` = `Left-Hippocampus`*100,
         `Right-Hippocampus` = `Right-Hippocampus`*100)

Now that things have been adjusted I need to do correlations

total HP

** position Error **

  • Position error avg is not significantly associated with t1 total hippocampus R=0.019, p=0.91
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • Position error rad3 is not significantly associated with t1 total hippocampus R=-0.04, p=0.81
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).


** Angular Error**

  • Angular error is not significantly associated with t1 total hippocampus R=0.095, p=0.55
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • Angular error is not significantly associated with t1 total hippocampus R=-0.06, p=0.72
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

** Degrees Traveled **

  • Degrees Traveled is not significantly associated with t1 total hippocampus R=-0.083, p=0.6
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • degrees traveled is not significantly associated with t1 total hippocampus R=-0.067, p=0.68
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "avg_t1_hipp", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled at 3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

Left HP

** position Error **

  • Position error avg is not significantly associated with t1 left hippocampus R=0.018, p=0.91
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • Position error rad3 is not significantly associated with t1 left hippocampus R=-0.01, p=0.95
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).


** Angular Error**

  • Angular error is not significantly associated with t1 left hippocampus R=0.098, p=0.53
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • Angular error is not significantly associated with t1 total hippocampus R=-0.37, p=0.82
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

** Degrees Traveled **

  • Degrees Traveled is not significantly associated with t1 Left-Hippocampus R=-0.069, p=0.66
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • degrees traveled is not significantly associated with t1 Left-Hippocampus R=-0.045, p=0.79
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Left-Hippocampus", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled at 3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

Right HP

** position Error **

  • Position error avg is not significantly associated with t1 Right hippocampus R=0.017, p=0.91
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • Position error rad3 is not significantly associated with t1 right hippocampus R=-0.071, p=0.67
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).


** Angular Error**

  • Angular error is not significantly associated with t1 right hippocampus R=0.084, p=0.59
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • Angular error is not significantly associated with t1 right hippocampus R=-0.082, p=0.62
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

** Degrees Traveled **

  • Degrees Traveled is not significantly associated with t1 Right-Hippocampus R=-0.092, p=0.56
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled  at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'

  • degrees traveled is not significantly associated with t1 Right-Hippocampus R=-0.074, p=0.65
# Use hp data frame adjusted 
ggscatter(midlife_HP_df_adj, x = "Right-Hippocampus", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "degrees traveled at 3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).

Partial correlations using TIV as covariate

Total hp

** Position Error ** - Position error is not significantly associated with t1 total hippocampus R=-0.63, p=0.53

# Use hp data frame adjusted 
pcor.test(midlife_HP_df$loop_pe_avg_m, midlife_HP_df$avg_t1_hipp, midlife_HP_df$eTIV)
##      estimate   p.value  statistic  n gp  Method
## 1 -0.09949911 0.5307073 -0.6324259 43  1 pearson
  • Position error rad3 is not significantly associated with t1 total hippocampus R=-0.87, p=0.39
# Use hp data frame adjusted 
midlife_hp_rad3 <- midlife_HP_df %>% filter(!is.na(loop_pe_rad3_m))
pcor.test(midlife_hp_rad3$loop_pe_rad3_m, midlife_hp_rad3$avg_t1_hipp, midlife_hp_rad3$eTIV)
##     estimate  p.value  statistic  n gp  Method
## 1 -0.1429717 0.391828 -0.8667343 39  1 pearson


** Angular Error **

  • Angular error is not significantly associated with t1 total hippocampus R=-0.36, p=0.72
# Use hp data frame adjusted 
pcor.test(midlife_HP_df$loop_ae_avg_degree, midlife_HP_df$avg_t1_hipp, midlife_HP_df$eTIV)
##     estimate   p.value  statistic  n gp  Method
## 1 -0.0569105 0.7203568 -0.3605179 43  1 pearson
  • angular error rad3 is not significantly associated with t1 total hippocampus R=-0.9, p=0.37
# Use hp data frame adjusted 
midlife_hp_rad3 <- midlife_HP_df %>% filter(!is.na(loop_ae_rad3_degree))
pcor.test(midlife_hp_rad3$loop_ae_rad3_degree, midlife_hp_rad3$avg_t1_hipp, midlife_hp_rad3$eTIV)
##     estimate   p.value  statistic  n gp  Method
## 1 -0.1494153 0.3706105 -0.9066698 39  1 pearson

** Degrees Traveled **

  • Degrees Traveled is not significantly associated with t1 total hippocampus R=-0.94, p=0.35
# Use hp data frame adjusted 
pcor.test(midlife_HP_df$loop_de_avg_degree, midlife_HP_df$avg_t1_hipp, midlife_HP_df$eTIV)
##     estimate   p.value  statistic  n gp  Method
## 1 -0.1479331 0.3498185 -0.9460198 43  1 pearson

-Degrees Traveled rad3 is not significantly associated with t1 total hippocampus R= -0.96, p=0.34

# Use hp data frame adjusted 
midlife_hp_rad3 <- midlife_HP_df %>% filter(!is.na(loop_de_rad3_degree))
pcor.test(midlife_hp_rad3$loop_de_rad3_degree, midlife_hp_rad3$avg_t1_hipp, midlife_hp_rad3$eTIV)
##     estimate   p.value  statistic  n gp  Method
## 1 -0.1583098 0.3424721 -0.9619898 39  1 pearson

Young

For young adults hippocampal., we will use freesurfer and VBM. We need to put things into scale with the midlife

Creating HP specific Dataframe : young

Total N - 31

# Let's create a clean df to work with here and include only the columns we want 

young_HP_df <-
 young_raw_df %>%  dplyr::select(
    "subject_id",
    "sex",
    "age_spatial_years",
    "loop_pe_rad3_m",
    "loop_pe_avg_m",
    "loop_de_rad3_degree",
    "loop_de_avg_degree",
    "loop_ae_rad3_degree",
    "loop_ae_avg_degree",
    "Left-Hippocampus",
    "Right-Hippocampus",
    "eTIV",
    "VaisTIV_VBM"
  ) %>% mutate(avg_t1_hipp = (.$`Left-Hippocampus` + .$`Right-Hippocampus`) /
                 2) %>% filter(!is.na(eTIV)) # Need to make sure we remove subj without scan # N=43
  
young_HP_female_df <- young_HP_df %>% filter(sex=="Female")

young_HP_male_df <- young_HP_df %>%  filter(sex== "Male")

Checking normality

knitr::kable(normality_young_HP) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "300px")
statistic pvalue method variable
0.891289448362091 0.0406097060033547 Shapiro-Wilk normality test young_HP_df\(loop_pe_rad3_m </td> </tr> <tr> <td style="text-align:left;"> 0.917622398774867 </td> <td style="text-align:left;"> 0.410869741382047 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_female_df\)loop_pe_rad3_m
0.819554493642225 0.0250296107499683 Shapiro-Wilk normality test young_HP_male_df\(loop_pe_rad3_m </td> </tr> <tr> <td style="text-align:left;"> 0.924692504181416 </td> <td style="text-align:left;"> 0.031503217012436 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_df\)loop_pe_avg_m
0.942325549335712 0.548211885252153 Shapiro-Wilk normality test young_HP_female_df\(loop_pe_avg_m </td> </tr> <tr> <td style="text-align:left;"> 0.847440833424306 </td> <td style="text-align:left;"> 0.00483057995234475 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_male_df\)loop_pe_avg_m
0.944415104032301 0.344201630583619 Shapiro-Wilk normality test young_HP_df\(loop_de_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.938516537642211 </td> <td style="text-align:left;"> 0.596545695693001 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_female_df\)loop_de_rad3_degree
0.8802028757242 0.131195427127208 Shapiro-Wilk normality test young_HP_male_df\(loop_de_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.955146425146202 </td> <td style="text-align:left;"> 0.216072493905156 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_df\)loop_de_avg_degree
0.919874033596885 0.317592303908437 Shapiro-Wilk normality test young_HP_female_df\(loop_de_avg_degree </td> </tr> <tr> <td style="text-align:left;"> 0.924156119557848 </td> <td style="text-align:left;"> 0.119146358186268 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_male_df\)loop_de_avg_degree
0.829226875499169 0.00405239215676323 Shapiro-Wilk normality test young_HP_df\(loop_ae_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.869160922556705 </td> <td style="text-align:left;"> 0.147906350957043 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_female_df\)loop_ae_rad3_degree
0.743377568430897 0.0029639851230227 Shapiro-Wilk normality test young_HP_male_df\(loop_ae_rad3_degree </td> </tr> <tr> <td style="text-align:left;"> 0.890867713779674 </td> <td style="text-align:left;"> 0.00429709005526902 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_df\)loop_ae_avg_degree
0.918178775417547 0.303762177188411 Shapiro-Wilk normality test young_HP_female_df\(loop_ae_avg_degree </td> </tr> <tr> <td style="text-align:left;"> 0.825228129557514 </td> <td style="text-align:left;"> 0.00210709055367868 </td> <td style="text-align:left;"> Shapiro-Wilk normality test </td> <td style="text-align:left;"> young_HP_male_df\)loop_ae_avg_degree 



young_loop_summarystats <- young_HP_df  %>% 
  group_by(sex) %>% 
  summarize(n_subject = n(),
            age_mean = mean(age_spatial_years),
            age_Sd = sd(age_spatial_years),
            AE_rad3 = mean(loop_ae_rad3_degree,na.rm=TRUE),
            AE_avg = mean(loop_ae_avg_degree ,na.rm=TRUE),
            PE_rad3 = mean(loop_pe_rad3_m,na.rm=TRUE),
            PE_avg = mean(loop_pe_avg_m,na.rm=TRUE),
            DT_rad3 = mean(loop_de_rad3_degree,na.rm=TRUE),
            DT_avg = mean(loop_de_avg_degree,na.rm=TRUE)) %>% as.data.frame() 
knitr::kable(young_loop_summarystats) %>% kable_styling(bootstrap_options =  c("striped", "hover", "condensed")) %>% scroll_box(width = "800px", height = "200")
sex n_subject age_mean age_Sd AE_rad3 AE_avg PE_rad3 PE_avg DT_rad3 DT_avg
Female 11 20.81818 2.561959 63.95039 52.86314 2.851491 1.589096 362.7044 368.8774
Male 20 20.25000 2.531382 54.86657 44.32274 2.487910 1.273323 352.4536 364.8809

Adjust TIV correlations

Shuying originally did an adjustment. so that’s waht we’re doing below. First we need to correct the T1 hippocampal volumes for TIV. T1 volumes and TIV are coming from freesurfers recon all.

# Okay so we need to do a bit of changing here by bringing our VBM to scale with midlife 
young_HP_df_adj <- young_HP_df %>%  mutate(VaisTIV_VBM = VaisTIV_VBM*1000)


#v contains adjusted hip
# now we create the function for adjusting by TIV
# 1 Create function for apply to variables
Young_dividebyTIV <- function(x, na.rm = FALSE) (x/young_HP_df_adj$VaisTIV_VBM)

# 2 Let's correct by mutating the columns using the TIV from freesurfer

young_HP_df_adj <- young_HP_df_adj %>% mutate_at(vars(avg_t1_hipp, `Left-Hippocampus`, `Right-Hippocampus`),
            Young_dividebyTIV) %>% 
  
  # multiplying to get proportions 
   mutate(avg_t1_hipp = avg_t1_hipp*1000,
         `Left-Hippocampus` = `Left-Hippocampus`*1000,
         `Right-Hippocampus` = `Right-Hippocampus`*1000)
Total HP

** position Error **

  • position error is not associated with T1 hippocampal volume in young adults R=-0.061, p=0.75 -not normal using spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Position error rad3 is not associated with position error in young adults R=-0.052, p=0.84
  • not normal, using spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).


** Angular Error **

  • Angular error is not associated with T1 hippocampal volume in young adults R=-0.068, p=0.72 -not normal use spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Angular error rad3 is not associated with position error in young adults. R=-0.52, p=0.84
  • not normal using spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).

** Degrees Traveled **

  • Degrees Traveled is not associated with T1 hippocampal volume in young adults R= - 0.2, p=0.28
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Degrees Traveled at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Angular error rad3 is not associated with position error in young adults. R=-0.17, p=0.52
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "avg_t1_hipp", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Degrees Traveled at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).

Left

** position Error **

  • position error is not associated with T1 left hippocampal volume in young adults R=-0.072, p=0.71
  • PE not normal use spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_pe_avg_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Position error rad3 is not associated with left hippocampal volume in young adults R=-0.012, p=0.65
  • not normal use spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_pe_rad3_m",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Position Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).


** Angular Error **

  • Angular error is not associated with T1 left hippocampal volume in young adults R=-0.099, p=0.6
  • notnormal use spearman
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_ae_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Angular error rad3 is not associated with left hippocampal volume in young adults. R=0.012, p=0.65

-not normal use spearman

# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_ae_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "spearman",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Angular Error at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).

** Degrees Traveled **

  • Degrees Traveled is not associated with T1 left hippocampal volume in young adults R= -0.18, p=0.34
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_de_avg_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Degrees Traveled at average (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).

  • Angular error rad3 is not associated with left hippocampal volume in young adults. R=-0.092, p=0.72
# Use hp data frame adjusted 
ggscatter(young_HP_df_adj, x = "Left-Hippocampus", y = "loop_de_rad3_degree",
          add = "reg.line",
          add.params = list(color = "black", fill = "lightgray"), # Customize reg. line
          cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
          conf.int = TRUE, 
          cor.method = "pearson",
          cor.coeff.args = list(label.sep = "\n"),
          xlab = "Averaged GMV Volume", ylab = "Degrees Traveled at aver3.0  (m)", ) +
    theme(legend.position = "top", legend.title=element_blank()) 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 14 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 14 rows containing missing values (`geom_point()`).